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Proceedings of the twenty-first international conference on Machine learning最新文献

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Using relative novelty to identify useful temporal abstractions in reinforcement learning 利用相对新颖性识别强化学习中有用的时间抽象
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015353
Özgür Simsek, A. Barto
{"title":"Using relative novelty to identify useful temporal abstractions in reinforcement learning","authors":"Özgür Simsek, A. Barto","doi":"10.1145/1015330.1015353","DOIUrl":"https://doi.org/10.1145/1015330.1015353","url":null,"abstract":"We present a new method for automatically creating useful temporal abstractions in reinforcement learning. We argue that states that allow the agent to transition to a different region of the state space are useful subgoals, and propose a method for identifying them using the concept of relative novelty. When such a state is identified, a temporally-extended activity (e.g., an option) is generated that takes the agent efficiently to this state. We illustrate the utility of the method in a number of tasks.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"1043 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123143681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 231
A hierarchical method for multi-class support vector machines 多类支持向量机的分层方法
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015427
Volkan Vural, Jennifer G. Dy
{"title":"A hierarchical method for multi-class support vector machines","authors":"Volkan Vural, Jennifer G. Dy","doi":"10.1145/1015330.1015427","DOIUrl":"https://doi.org/10.1145/1015330.1015427","url":null,"abstract":"We introduce a framework, which we call Divide-by-2 (DB2), for extending support vector machines (SVM) to multi-class problems. DB2 offers an alternative to the standard one-against-one and one-against-rest algorithms. For an N class problem, DB2 produces an N − 1 node binary decision tree where nodes represent decision boundaries formed by N − 1 SVM binary classifiers. This tree structure allows us to present a generalization and a time complexity analysis of DB2. Our analysis and related experiments show that, DB2 is faster than one-against-one and one-against-rest algorithms in terms of testing time, significantly faster than one-against-rest in terms of training time, and that the cross-validation accuracy of DB2 is comparable to these two methods.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116778753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 154
Optimising area under the ROC curve using gradient descent 采用梯度下降法优化ROC曲线下面积
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015366
A. Herschtal, Bhavani Raskutti
{"title":"Optimising area under the ROC curve using gradient descent","authors":"A. Herschtal, Bhavani Raskutti","doi":"10.1145/1015330.1015366","DOIUrl":"https://doi.org/10.1145/1015330.1015366","url":null,"abstract":"This paper introduces RankOpt, a linear binary classifier which optimises the area under the ROC curve (the AUC). Unlike standard binary classifiers, RankOpt adopts the AUC statistic as its objective function, and optimises it directly using gradient descent. The problems with using the AUC statistic as an objective function are that it is non-differentiable, and of complexity O(n2) in the number of data observations. RankOpt uses a differentiable approximation to the AUC which is accurate, and computationally efficient, being of complexity O(n.) This enables the gradient descent to be performed in reasonable time. The performance of RankOpt is compared with a number of other linear binary classifiers, over a number of different classification problems. In almost all cases it is found that the performance of RankOpt is significantly better than the other classifiers tested.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"5 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117229944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 230
Parameter space exploration with Gaussian process trees 高斯过程树的参数空间探索
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015367
R. Gramacy, Herbert K. H. Lee, W. Macready
{"title":"Parameter space exploration with Gaussian process trees","authors":"R. Gramacy, Herbert K. H. Lee, W. Macready","doi":"10.1145/1015330.1015367","DOIUrl":"https://doi.org/10.1145/1015330.1015367","url":null,"abstract":"Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are unnecessary in regions where the response is easy predicted; well-chosen designs could allow a mapping of the response with far fewer simulation runs. Thus, there is a need for computationally inexpensive surrogate models and an accompanying method for selecting small designs. We explore a general methodology for addressing this need that uses non-stationary Gaussian processes. Binary trees partition the input space to facilitate non-stationarity and a Bayesian interpretation provides an explicit measure of predictive uncertainty that can be used to guide sampling. Our methods are illustrated on several examples, including a motivating example involving computational fluid dynamics simulation of a NASA reentry vehicle.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"44 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120941198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 71
Learning probabilistic motion models for mobile robots 移动机器人的概率运动模型学习
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015413
A. Eliazar, Ronald E. Parr
{"title":"Learning probabilistic motion models for mobile robots","authors":"A. Eliazar, Ronald E. Parr","doi":"10.1145/1015330.1015413","DOIUrl":"https://doi.org/10.1145/1015330.1015413","url":null,"abstract":"Machine learning methods are often applied to the problem of learning a map from a robot's sensor data, but they are rarely applied to the problem of learning a robot's motion model. The motion model, which can be influenced by robot idiosyncrasies and terrain properties, is a crucial aspect of current algorithms for Simultaneous Localization and Mapping (SLAM). In this paper we concentrate on generating the correct motion model for a robot by applying EM methods in conjunction with a current SLAM algorithm. In contrast to previous calibration approaches, we not only estimate the mean of the motion, but also the interdependencies between motion terms, and the variances in these terms. This can be used to provide a more focused proposal distribution to a particle filter used in a SLAM algorithm, which can reduce the resources needed for localization while decreasing the chance of losing track of the robot's position. We validate this approach by recovering a good motion model despite initialization with a poor one. Further experiments validate the generality of the learned model in similar circumstances.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125854652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 76
Delegating classifiers 授权分类器
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015395
C. Ferri, Peter A. Flach, J. Hernández-Orallo
{"title":"Delegating classifiers","authors":"C. Ferri, Peter A. Flach, J. Hernández-Orallo","doi":"10.1145/1015330.1015395","DOIUrl":"https://doi.org/10.1145/1015330.1015395","url":null,"abstract":"A sensible use of classifiers must be based on the estimated reliability of their predictions. A cautious classifier would delegate the difficult or uncertain predictions to other, possibly more specialised, classifiers. In this paper we analyse and develop this idea of delegating classifiers in a systematic way. First, we design a two-step scenario where a first classifier chooses which examples to classify and delegates the difficult examples to train a second classifier. Secondly, we present an iterated scenario involving an arbitrary number of chained classifiers. We compare these scenarios to classical ensemble methods, such as bagging and boosting. We show experimentally that our approach is not far behind these methods in terms of accuracy, but with several advantages: (i) improved efficiency, since each classifier learns from fewer examples than the previous one; (ii) improved comprehensibility, since each classification derives from a single classifier; and (iii) the possibility to simplify the overall multi-classifier by removing the parts that lead to delegation.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123714382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 86
A theoretical characterization of linear SVM-based feature selection 基于线性支持向量机的特征选择的理论表征
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015421
D. Hardin, I. Tsamardinos, C. Aliferis
{"title":"A theoretical characterization of linear SVM-based feature selection","authors":"D. Hardin, I. Tsamardinos, C. Aliferis","doi":"10.1145/1015330.1015421","DOIUrl":"https://doi.org/10.1145/1015330.1015421","url":null,"abstract":"Most prevalent techniques in Support Vector Machine (SVM) feature selection are based on the intuition that the weights of features that are close to zero are not required for optimal classification. In this paper we show that indeed, in the sample limit, the irrelevant variables (in a theoretical and optimal sense) will be given zero weight by a linear SVM, both in the soft and the hard margin case. However, SVM-based methods have certain theoretical disadvantages too. We present examples where the linear SVM may assign zero weights to strongly relevant variables (i.e., variables required for optimal estimation of the distribution of the target variable) and where weakly relevant features (i.e., features that are superfluous for optimal feature selection given other features) may get non-zero weights. We contrast and theoretically compare with Markov-Blanket based feature selection algorithms that do not have such disadvantages in a broad class of distributions and could also be used for causal discovery.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126654669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 63
Convergence of synchronous reinforcement learning with linear function approximation 线性函数逼近同步强化学习的收敛性
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015390
A. Merke, Ralf Schoknecht
{"title":"Convergence of synchronous reinforcement learning with linear function approximation","authors":"A. Merke, Ralf Schoknecht","doi":"10.1145/1015330.1015390","DOIUrl":"https://doi.org/10.1145/1015330.1015390","url":null,"abstract":"Synchronous reinforcement learning (RL) algorithms with linear function approximation are representable as inhomogeneous matrix iterations of a special form (Schoknecht & Merke, 2003). In this paper we state conditions of convergence for general inhomogeneous matrix iterations and prove that they are both necessary and sufficient. This result extends the work presented in (Schoknecht & Merke, 2003), where only a sufficient condition of convergence was proved. As the condition of convergence is necessary and sufficient, the new result is suitable to prove convergence and divergence of RL algorithms with function approximation. We use the theorem to deduce a new concise proof of convergence for the synchronous residual gradient algorithm (Baird, 1995). Moreover, we derive a counterexample for which the uniform RL algorithm (Merke & Schoknecht, 2002) diverges. This yields a negative answer to the open question if the uniform RL algorithm converges for arbitrary multiple transitions.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127022786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Multi-task feature and kernel selection for SVMs 支持向量机的多任务特征和核选择
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015426
T. Jebara
{"title":"Multi-task feature and kernel selection for SVMs","authors":"T. Jebara","doi":"10.1145/1015330.1015426","DOIUrl":"https://doi.org/10.1145/1015330.1015426","url":null,"abstract":"We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is advantageous when multiple classification tasks and differently labeled datasets exist over a common input space. Different datasets can mutually reinforce a common choice of representation or relevant features for their various classifiers. We derive a multi-task representation learning approach using the maximum entropy discrimination formalism. The resulting convex algorithms maintain the global solution properties of support vector machines. However, in addition to multiple SVM classification/regression parameters they also jointly estimate an optimal subset of features or optimal combination of kernels. Experiments are shown on standardized datasets.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116403633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 251
Robust feature induction for support vector machines 支持向量机的鲁棒特征归纳
Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015370
Rong Jin, Huan Liu
{"title":"Robust feature induction for support vector machines","authors":"Rong Jin, Huan Liu","doi":"10.1145/1015330.1015370","DOIUrl":"https://doi.org/10.1145/1015330.1015370","url":null,"abstract":"The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, nonlinear features are introduced into a support vector machine (SVM) through a nonlinear kernel function. One disadvantage of such an approach is that the feature space induced by a kernel function is usually of high dimension and therefore will substantially increase the chance of over-fitting the training data. Another disadvantage is that nonlinear features are induced implicitly and therefore are difficult for people to understand which induced features are critical to the classification performance. In this paper, we propose a boosting-style algorithm that can explicitly induces important nonlinear features for SVMs. We present empirical studies with discussion to show that this approach is effective in improving classification accuracy for SVMs. The comparison with an SVM model using nonlinear kernels also indicates that this approach is effective and robust, particularly when the number of training data is small.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"1930 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128801658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
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